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Handling Sparse Non-negative Data in Finance

Agostino Capponi, Zhaonan Qu

arXiv 1 Sep 2025 · Econometrics

arXiv:2509.01478 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We show that Poisson regression, though often recommended over log-linear regression for modeling count and other non-negative variables in finance and economics, can be far from optimal when heteroskedasticity and sparsity -- two common features of such data -- are both present. We propose a general class of moment estimators, encompassing Poisson regression, that balances the bias-variance trade-off under these conditions. A simple cross-validation procedure selects the optimal estimator. Numerical simulations and applications to corporate finance data reveal that the best choice varies substantially across settings and often departs from Poisson regression, underscoring the need for a more flexible estimation framework.

Citation extraction

55
references
124
in-text mentions
55
distinct cited
1
self-citations
18,586
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Santos Silva, J., Tenreyro, S (2006) The log of gravity1.000146100%
2Cohn, J.B., Liu, Z., Wardlaw, M.I (2022) Count (and count-like) data in finance1.000104100%
3Hirshleifer, D., Low, A., Teoh, S.H (2012) Are overconfident ceos better innovators?1.00073100%
4Gourieroux, C., Monfort, A., Trognon, A (1984) Pseudo maximum likelihood methods: Theory1.00065100%
5Santos Silva, J., Tenreyro, S (2011) Further simulation evidence on the performance of the poisson pseudo-maximum likelihood estimator1.00053100%
6Mullahy, J (1986) Specification and testing of some modified count data models0.92844100%
7Gourieroux, C., Monfort, A., Trognon, A (1984) Pseudo maximum likelihood methods: Applications to poisson models0.92843100%
8Lambert, D (1992) Zero-inflated poisson regression, with an application to defects in manufacturing0.92843100%
9Park, R.E (1966) Estimation with heteroscedastic error terms0.84333100%
10Bekkerman, R., Cohen, M.C., Kung, E., Maiden, J., Proserpio, D (2023) The effect of short-term rentals on residential investment0.81142100%

Showing the top 10 of 55 scored citations.